Unit testing Spark transformations in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark DataFrame from a list of tuples.

Apache Spark

df = spark.createDataFrame([1], ['name', 'age'])

Drag options to blanks, or click blank then click option'

A[('Alice', 30), ('Bob', 25)]

B['name', 'age']

C('Alice', 30, 'Bob', 25)

D{'Alice': 30, 'Bob': 25}

Attempts:

3 left

💡 Hint

Common Mistakes

Using a dictionary instead of a list of tuples.

Passing column names as data.

To create a DataFrame, you need a list of tuples representing rows.

2fill in blank

medium

Complete the code to filter rows where age is greater than 25.

Apache Spark

filtered_df = df.filter(df.age [1] 25)

Drag options to blanks, or click blank then click option'

A==

B>

C<=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>'

Using '==' which filters only age equal to 25.

The filter condition should select rows with age greater than 25.

3fill in blank

hard

Fix the error in the code to select the 'name' column as a list.

Apache Spark

names = df.select([1]).rdd.flatMap(lambda x: x).collect()

Drag options to blanks, or click blank then click option'

A'age'

Bdf.name

C'name'

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column object instead of string.

Using variable name without quotes.

The select method requires the column name as a string.

4fill in blank

hard

Fill both blanks to create a dictionary of names and ages for people older than 20.

Apache Spark

result = [1].rdd.filter(lambda x: x.[2] > 20).collectAsMap()

Drag options to blanks, or click blank then click option'

Adf.select('name', 'age')

Bdf.select('age')

Cage

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only one column.

Using wrong attribute in lambda.

Select both 'name' and 'age' columns, then filter by 'age' attribute in lambda.

5fill in blank

hard

Fill all three blanks to create a unit test that checks if the filtered DataFrame has the expected count.

Apache Spark

def test_filter_count():
    filtered = df.filter(df.[1] [2] [3])
    assert filtered.count() == 1

Drag options to blanks, or click blank then click option'

Aage

B>

C25

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column name.

Using wrong comparison operator.

Using wrong value for filtering.

The test filters rows where age is greater than 25 and asserts the count is 1.

Practice

1. What is the main purpose of unit testing Spark transformations?

easy

A. To check small parts of Spark code for errors early

B. To improve the speed of Spark jobs

C. To deploy Spark applications automatically

D. To visualize Spark data outputs

Solution

1. Step 1: Understand unit testing purpose

   Unit testing focuses on checking small parts of code to find errors early.

2. Step 2: Relate to Spark transformations

   Testing Spark transformations means verifying each step works correctly before full runs.

3. Final Answer:

   To check small parts of Spark code for errors early -> Option A

4. Quick Check:

   Unit testing = Early error detection [OK]

Hint: Unit tests catch errors early in small code parts [OK]

Common Mistakes:

• Confusing unit testing with performance tuning
• Thinking unit tests deploy code
• Assuming unit tests create visualizations

2. Which of the following is the correct way to compare actual and expected DataFrames in a Spark unit test?

easy

A. print(actual_df) == print(expected_df)

B. assert actual_df.collect() == expected_df.collect()

C. actual_df.show() == expected_df.show()

D. actual_df.count() != expected_df.count()

Solution

1. Step 1: Understand DataFrame comparison

   Comparing DataFrames requires checking their collected data, not just printing or counts.

2. Step 2: Identify correct assertion

   Using assert with collected rows compares actual data content correctly.

3. Final Answer:

   assert actual_df.collect() == expected_df.collect() -> Option B

4. Quick Check:

   Compare collected data for equality [OK]

Hint: Use collect() to compare DataFrame contents in tests [OK]

Common Mistakes:

• Comparing printed output instead of data
• Using show() which returns None
• Comparing only row counts without data

3. Given the following Spark code in a unit test:

input_df = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])
transformed_df = input_df.filter("id > 1")
result = transformed_df.collect()

What is the value of result?

medium

A. [Row(id=1, value='a')]

B. []

C. [Row(id=1, value='a'), Row(id=2, value='b')]

D. [Row(id=2, value='b')]

Solution

1. Step 1: Understand the filter condition

   The filter "id > 1" keeps rows where id is greater than 1, so only id=2 remains.

2. Step 2: Collect the filtered rows

   Collect returns a list of Row objects matching the filter, here only one row with id=2 and value='b'.

3. Final Answer:

   [Row(id=2, value='b')] -> Option D

4. Quick Check:

   Filter id > 1 keeps only id=2 [OK]

Hint: Filter keeps rows matching condition before collect [OK]

Common Mistakes:

• Including rows with id=1 mistakenly
• Confusing collect output format
• Assuming filter excludes id=2

4. You wrote this unit test code:

expected_df = spark.createDataFrame([(1, 'x')], ['id', 'value'])
actual_df = spark.createDataFrame([(1, 'x')], ['id', 'value'])
assert expected_df == actual_df

Why does this test fail?

medium

A. DataFrames cannot be compared directly with '=='

B. The schemas of DataFrames are different

C. The data in DataFrames is different

D. Spark session is not initialized

Solution

1. Step 1: Understand DataFrame equality

   Spark DataFrames do not support direct '==' comparison; it checks object identity, not content.

2. Step 2: Correct way to compare DataFrames

   Use methods like collect() or exceptAll() to compare data content, not '=='.

3. Final Answer:

   DataFrames cannot be compared directly with '==' -> Option A

4. Quick Check:

   Use collect() for DataFrame equality, not '==' [OK]

Hint: Never use '==' to compare DataFrames directly [OK]

Common Mistakes:

• Assuming '==' compares data content
• Ignoring schema differences
• Forgetting to initialize Spark session

5. You want to unit test a Spark transformation that removes duplicate rows based on the 'id' column, keeping the first occurrence. Which approach correctly tests this transformation?

hard

A. Create input DataFrame without duplicates, apply transformation, assert output equals input

B. Create input DataFrame with duplicates, apply transformation, assert output count equals input count

C. Create input DataFrame with duplicates, apply transformation, assert output equals expected DataFrame without duplicates

D. Create input DataFrame with duplicates, apply transformation, assert output is empty

Solution

1. Step 1: Prepare input with duplicates

   To test duplicate removal, input must contain duplicate rows on 'id'.

2. Step 2: Apply transformation and compare output

   After transformation, output should have duplicates removed; compare with expected DataFrame without duplicates.

3. Final Answer:

   Create input DataFrame with duplicates, apply transformation, assert output equals expected DataFrame without duplicates -> Option C

4. Quick Check:

   Test removes duplicates by comparing expected output [OK]

Hint: Test duplicate removal by comparing to expected no-duplicate DataFrame [OK]

Common Mistakes:

• Testing with no duplicates in input
• Comparing only row counts, not content
• Expecting empty output after removal